AP Selection Algorithm with Adaptive CCAT for Dense Wireless Networks
Ye Na Kim, Munsuk Kim, SuKyoung Lee, David W. Griffith, Nada T. Golmie
Wireless Local Area Networks (WLANs)-enabled devices are now everywhere and their rapid spread has created dense deployment environments. For such dense WLANs, the High Efficiency WLAN Study Group was formed, and as an extension of their activity, effort on standardization of IEEE 802.11ax Task Group (TG) was initiated. The goal of the TG on IEEE 802.11ax is to improve per- node throughput of WLAN dense networks in the presence of interfering sources. To attain this aim, the TG is currently working on Clear Channel Assess- ment Threshold (CCAT) adjustment. As the CCAT is increased, more concurrent transmissions are permitted, leading to more interference. By using a small CCAT, the amount of interference can be reduced, but the transmission opportunity decays. Thus, we propose an algorithm that adjusts CCAT based on the co- channel interference and transmission opportunity for network capacity improvement in dense WLANs. In addition, traffic load may not be fairly shared by all serving APs due to the typical Received Signal Strength (RSS)-based AP selection algorithm. In this paper, therefore, we propose an Access Point (AP) selection algorithm that chooses both AP and CCAT providing the highest achievable throughput for a STA by considering the co-channel interference as well as the traffic load status in dense WLANs. Simulation results show that our proposed algorithm achieves better performance in terms of throughput and Jain's Fairness Index (JFI) in dense wireless networks with two channel assignment methods in three scenarios.
2017 IEEE Wireless Communications and Networking Conference (WCNC)
, Kim, M.
, Lee, S.
, Griffith, D.
and Golmie, N.
AP Selection Algorithm with Adaptive CCAT for Dense Wireless Networks, 2017 IEEE Wireless Communications and Networking Conference (WCNC), San Francisco, CA, US, [online], https://doi.org/10.1109/WCNC.2017.7925822, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=922012
(Accessed December 1, 2023)